Conferring Company Financial Distress From Bank Lending Information

[[abstract]]本研究是以Logitistic迴歸模型建構財務危機預警模型，研究樣本期間從88年至92年9月，共選取了75家上市危機公司，並且配對75家產業相同、規模相當的上市正常公司。本研究的變數除使用常見財務比率變數外，另外再加入銀行借款變數，如銀行借款比率、抽銀根比率與股利政策一變數。實證結果顯示在銀行借款比率與危機公司呈現正相關，危機公司的借款比率顯著比正常公司多；在股利變動方面，危機公司相較於正常公司有顯著股利減少(停發)的策略；由實證結果得知將這些變數納入模型，會增加模型的預測能力，因此可以從這些變數萃取一些端倪，達到事先預防的功能。[[abstract]]This study obtained 75 distress firms from 1999 to 2003, and we used these firms’ data to develop a logistic regression model. In constructing the model, there are three main categories of indicators, including of financial ratio categories, bank lending categories, and nonfinancial categories (dividend, CEO, and CPA). The study conducted a test of the usefulness of these categories to explain firms’ financial distress. We also used the changes in financial ratio categories to analyze if the odd ratio increased .The empirical results showed that bank lending ratio and dividend category can distinguish financial distress firms from healthy firms. Besides, the explaining ability of the prediction model can be improved by adding these categories